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# vistan `vistan` is a simple library to run variational inference algorithms on Stan models. Our primary aim is to help you quickly run variational methods from [Advances in BBVI](https://proceedings.neurips.cc/paper/2020/file/c91e3483cf4f90057d02aa492d2b25b1-Paper.pdf) on any Stan model. `vistan` uses [autograd](https://github.com/HIPS/autograd) and [PyStan](https://github.com/stan-dev/pystan) under the hood. ## Features > - **Initialization:** Laplace's method to initialize full-rank Gaussian > - **Gradient Estimators:** Total-gradient, STL, DReG, closed-form entropy > - **Variational Families:** Full-rank Gaussian, Diagonal Gaussian, RealNVP > - **Objectives:** ELBO, IW-ELBO > - **IW-sampling:** Posterior samples using importance weighting ## Installation ``` pip install vistan ``` ## Usage ### Meanfield VI ```python import vistan import matplotlib.pyplot as plt import numpy as np import scipy code = """ data { int<lower=0> N; int<lower=0,upper=1> x[N]; } parameters { real<lower=0,upper=1> p; } model { p ~ beta(1,1); x ~ bernoulli(p); } """ data = {"N":5, "x":[0,1,0,0,0]} algo = vistan.algorithm() # runs Meanfield VI by default posterior = algo(code, data) samples = posterior.sample(100000) points = np.arange(0,1,.01) plt.hist(samples['p'], 200, density = True, histtype = 'step') plt.plot(points,scipy.stats.beta(2,5).pdf(points),label='True Posterior') plt.legend() plt.show() ``` ### Gaussian VI We provide some default VI algorithm choices which can accessed using `method` argument. ```python algo = vistan.algorithm(method = 'gaussian') posterior = algo(code, data) samples = posterior.sample(100000) plt.hist(samples['p'], 200, density = True, histtype = 'step') plt.plot(points,scipy.stats.beta(2,5).pdf(points),label='True Posterior') plt.legend() plt.show() ``` ### Flow-based VI ```python algo = vistan.algorithm(method = 'flows') posterior = algo(code, data) samples = posterior.sample(100000) plt.hist(samples['p'], 200, density = True, histtype = 'step') plt.plot(points,scipy.stats.beta(2,5).pdf(points),label='True Posterior') plt.legend() plt.show() ``` ### ADVI ```python algo = vistan.algorithm(method = 'advi') posterior = algo(code, data) samples = posterior.sample(100000) plt.hist(samples['p'], 200, density = True, histtype = 'step') plt.plot(points,scipy.stats.beta(2,5).pdf(points),label='True Posterior') plt.legend() plt.show() ``` ### Custom You can also specify custom VI algorithms to work with your Stan models, just set the `method='custom'` and provide customized arguments. ```python algo = vistan.algorithm(method = 'custom', M_iw_train = 2, grad_estimator = "DReG", vi_family = "gaussian", per_iter_sample_budget = 10, max_iters = 100) posterior = algo(code, data) samples = posterior.sample(100000) plt.hist(samples['p'], 200, density = True, histtype = 'step') plt.plot(points,scipy.stats.beta(2,5).pdf(points),label='True Posterior') plt.legend() plt.show() ``` ### IW-sampling We provide support to use IW-sampling at inference time (see [Advances in BBVI](https://proceedings.neurips.cc/paper/2020/file/c91e3483cf4f90057d02aa492d2b25b1-Paper.pdf) for more information.) This importance weights `M_iw_sample` candidate samples and picks one final sample. ```python samples = posterior.sample(100000, M_iw_sample = 10) plt.hist(samples['p'], 200, density = True, histtype = 'step') plt.plot(points,scipy.stats.beta(2,5).pdf(points),label='True Posterior') plt.legend() plt.show() ``` ### Initialization We provide support to use Laplace's method to initialize the parameters for Gaussian VI. ```python algo = vistan.algorithm(method = 'gaussian', LI = True) posterior = algo(code, data) samples = posterior.sample(100000) plt.hist(samples['p'], 200, density = True, histtype = 'step') plt.plot(points,scipy.stats.beta(2,5).pdf(points),label='True Posterior') plt.legend() plt.show() ``` ### Building your own inference algorithms We provide access to the `model.log_prob` function we use internally for optimization. This allows you to evaluate the log density in the unconstrained space for your Stan mode. Also, this function is differentiable in `autograd`. ```python posterior = vistan.algorithm(max_iters = 1)(code, data) log_prob = posterior.model.log_prob ``` ## Limitations - We currently only support inference on all latent parameters in the model